A typical situation in HTA is that where a pharmaceutical company has individual patient data (IPD) from its own study comparing \(A\)  versus \(C\) , which we shall denote the index trial, but only published aggregate-level data (ALD) from another study comparing \(B\)  versus \(C\) , which we call the competitor trial. In this two-study scenario, cross-trial imbalances in effect measure modifiers, effect modifiers for short, make the standard indirect treatment comparisons [1]} vulnerable to bias [2]}. Novel covariate-adjusted indirect comparison methods have been introduced to account for these imbalances and provide equipoise to the comparison [3]}, [4]}, [5]}, [6]}, [7]}.
